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The Google Professional-Cloud-Database-Engineer exam tests the candidates' knowledge and skills in various areas related to database management, such as database design, data modeling, database administration, data security, and data analysis. It also covers topics related to Google Cloud Platform services, such as Cloud SQL, Cloud Spanner, Cloud Bigtable, and Cloud Datastore.

Google Professional-Cloud-Database-Engineer certification exam is designed for professionals who specialize in database management on the Google Cloud Platform. Google Cloud Certified - Professional Cloud Database Engineer certification is ideal for individuals who have experience in designing, developing, and managing cloud-native database solutions. Professional-Cloud-Database-Engineer Exam aims to evaluate the candidate's proficiency in database concepts, cloud-native database services, data security, and data migration.

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Google Professional-Cloud-Database-Engineer Exam Syllabus Topics:

Topic	Details
Topic 1	Analyze relevant variables to perform database capacity and usage planning Design scalable, highly available, and secure databases
Topic 2	Deploy scalable and highly available databases in Google Cloud Determine database connectivity and access management considerations
Topic 3	Determine the correct database migration tools for a given scenario Size database compute and storage based on performance requirements
Topic 4	Plan and perform database migration, including fallback plans and schema conversion Test high availability and disaster recovery strategies periodically
Topic 5	Distinguish between SQL and NoSQL business requirements Evaluate tradeoffs between multi-region, region, and zonal database deployment strategies
Topic 6	Provision high availability database solutions in Google Cloud Design scalable and highly available cloud database solutions
Topic 7	Automate database instance provisioning Determine how applications will connect to the database

Google Cloud Certified - Professional Cloud Database Engineer Sample Questions (Q119-Q124):

NEW QUESTION # 119
Your DevOps team is using Terraform to deploy applications and Cloud SQL databases. After every new application change is rolled out, the environment is torn down and recreated, and the persistent database layer is lost. You need to prevent the database from being dropped. What should you do?

A. Rerun terraform apply.
B. Use point-in-time-recovery (PITR) to recover the database.
C. Create a read replica.
D. Set Terraform deletion_protection to true.

Answer: D

Explanation:
https://cloud.google.com/docs/terraform/best-practices-for-terraform#stateful-resources

NEW QUESTION # 120
You are running an instance of Cloud Spanner as the backend of your ecommerce website. You learn that the quality assurance (QA) team has doubled the number of their test cases. You need to create a copy of your Cloud Spanner database in a new test environment to accommodate the additional test cases. You want to follow Google-recommended practices. What should you do?

A. Use Dataflow to run the export in Avro format.
B. Use Dataflow to run the export in text format.
C. Use Cloud Functions to run the export in text format.
D. Use Cloud Functions to run the export in Avro format.

Answer: A

NEW QUESTION # 121
You are the primary DBA of a Cloud SQL for PostgreSQL database that supports 6 enterprise applications in production. You used Cloud SQL Insights to identify inefficient queries and now need to identify the application that is originating the inefficient queries. You want to follow Google-recommended practices. What should you do?

A. Shut down and restart each application.
B. Use query tags to add application-centric database monitoring.
C. Write a utility to scan database query logs.
D. Write a utility to scan application logs.

Answer: B

NEW QUESTION # 122
You are deploying a Cloud SQL for MySQL database to serve a non-critical application. The database size is 10 GB and will be updated every night with data stored in a Cloud Storage bucket. The database serves read-only traffic from the application during the day. The data locality requirement of this application mandates that data must reside in a single region. You want to minimize the cost of running this database while maintaining an RTO of 1 day. What should you do-

A. Create a Cloud SQL for MySQL instance with high availability (HA) disabled. Create a read replica in the same zone.
B. Create a Cloud SQL for MySQL instance with high availability (HA) disabled. Create a read replica in a second region.
C. Create a Cloud SQL for MySQL instance with high availability (HA) enabled. Configure automated backups of the Cloud SQL instance and use the default backup location.
D. Create a Cloud SQL for MySQL instance with high availability (HA) disabled. Configure automated backups of the Cloud SQL instance and use a custom backup location to store backups in a Cloud Storage bucket in the same region.

Answer: D

Explanation:
Chosen option is the best approach:
* Single-region data locality: By pointing your automated backups at a region-scoped Cloud- Storage bucket, you ensure all data (including backups) stays in-region.
* RTO ≤-1-day: Automated daily backups let you restore from the most recent snapshot within your 24-hour RTO window.
* Cost minimized: Disabling HA and not spinning up any replicas avoids the extra instance costs.
You pay only for one primary instance plus your small storage charges for ~10-GB of backups.
* Simplicity: Cloud-SQL's built-in automated backups require zero operational overhead beyond the one-time bucket configuration.

NEW QUESTION # 123
You are building a data warehouse on BigQuery. Sources of data include several MySQL databases located on-premises.
You need to transfer data from these databases into BigQuery for analytics. You want to use a managed solution that has low latency and is easy to set up. What should you do?

A. Use Database Migration Service to replicate data to a Cloud SQL for MySQL instance. Create federated tables in BigQuery on top of the replicated instances to transform and load the data into your BigQuery database.
B. Create extracts from your on-premises databases periodically, and push these extracts to Cloud Storage. Upload the changes into BigQuery, and merge them with existing tables.
C. Use Cloud Data Fusion and scheduled workflows to extract data from MySQL. Transform this data into the appropriate schema, and load this data into your BigQuery database.
D. Use Datastream to connect to your on-premises database and create a stream. Have Datastream write to Cloud Storage. Then use Dataflow to process the data into BigQuery.

Answer: D

NEW QUESTION # 124
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